Zero-To-CAD-Qwen3-VL-2B
ADSKAILab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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Explore AI models 325
trocr-base-printed-fr
agomberto基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗trocr-large-handwritten-fr
agomberto基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗FormulaNet
alephpi基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mgp-str-base
alibaba-damoMGP-STR base-sized model is trained on MJSynth and SynthText. It was introduced in the paper Multi-Granularity Prediction for Scene Text Recognition and first released in this repository. MGP-STR is pure vision STR model, consisting of ViT and specially designed A^3 modules. The ViT module was initialized from the weights of DeiT-base, except the patch embedding model, due to the inconsistent input size. Images (32x128) are presented to the model as a sequence of fixed-size patches (resolution 4x4), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the ViT module. Next, A^3 module selects a meaningful combination from the tokens of ViT output and integrates them into one output token corresponding to a specific character. Moreover, subword classification heads based on BPE A^3 module and WordPiece A^3 module are devised for subword predictions, so that the language information can be implicitly modeled. Finally, these multi-granularity predictions (character, subword and even word) are merged via a simple and effective fusion strategy.
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View details ↗LiteALPR
anhone3基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗smt-camera-grandstaff
antoniorv6基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗smt-grandstaff
antoniorv6基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qota-OCR
atonlee基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Rukopys-OCR-4B-GGUF
Axway基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗indic-ocr
bodhan-ai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗pix2text-mfd-1.5
breezedeus基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗pix2text-mfr
breezedeusThis MFR model utilizes the TrOCR architecture developed by Microsoft, starting with its initial values and retrained using a dataset of mathematical formula images. The resulting MFR model can be used to convert images of mathematical formulas into LaTeX text representation. More detailed can be found: Pix2Text V1.0 New Release: The Best Open-Source Formula Recognition Model | Breezedeus.com. 此 MFR 模型使用了微软的 TrOCR 架构，以其为初始值并利用数学公式图片数据集进行了重新训练。 获得的 MFR 模型可用于把数学公式图片转换为 LaTeX 文本表示。更多细节请见：Pix2Text V1.0 新版发布：最好的开源公式识别模型 | Breezedeus.com。 This method requires the installation of pix2text, utilizing the Mathematical Formula Detection model (MFD) within Pix2Text. It is capable of recognizing not only pure formula images but also mixed images containing text.
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View details ↗pix2text-mfr-1.5
breezedeusThis MFR model utilizes the TrOCR architecture developed by Microsoft, starting with its initial values and retrained using a dataset of mathematical formula images. The resulting MFR model can be used to convert images of mathematical formulas into LaTeX text representation. More detailed can be found: Pix2Text V1.0 New Release: The Best Open-Source Formula Recognition Model | Breezedeus.com. 此 MFR 模型使用了微软的 TrOCR 架构，以其为初始值并利用数学公式图片数据集进行了重新训练。 获得的 MFR 模型可用于把数学公式图片转换为 LaTeX 文本表示。更多细节请见：Pix2Text V1.0 新版发布：最好的开源公式识别模型 | Breezedeus.com。 This method requires the installation of pix2text, utilizing the Mathematical Formula Detection model (MFD) within Pix2Text. It is capable of recognizing not only pure formula images but also mixed images containing text.
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View details ↗transcoda-59M-zeroshot-v1
btrkeks基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗sd3.5-large-turbo
calcuis基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗tiny-image-captioning
cnmoro基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗pix2tex-mfr-gguf
cstr基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Apache-2.0 Tesseract LSTM line-recognition models converted to GGUF for CrispEmbed. The repository contains English, German, French, Spanish, Italian, Portuguese, Dutch, Russian, Arabic, Simplified Chinese, Japanese, and Korean variants. These are line recognizers: normalize and crop individual text lines before recognition, or pair them with a detector such as DBNet for full-page OCR. Each language has an F32 reference, an F16 deployment model, and Q8_0/Q4_K variants. The quantized models preserve `output.weight` and `output.bias` at the source precision (F16 for the multilingual F16-derived models; F32 for German regenerated from its original `.traineddata`). Only recurrent matrices are quantized. This preserves the CTC decision boundary and keeps critical character logits stable.
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗indra-ocr
datalab-toIndra OCR is a 1M-parameter OCR model that runs on CPU. It turns PDFs and images into markdown, text, or JSON with boxes, and it reads handwriting, 80+ languages, and pages at any angle. Indra OCR is built for speed on CPUs. When you need the highest accuracy, on handwriting, math, complex tables and degraded scans, our managed platform runs Chandra, our state of the art OCR model, with zero data retention by default, SOC 2 Type 2, and custom BAAs. If you have high volume workloads, we offer a batch processing service that can handle 1B+ pages a day.
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View details ↗ko-trocr
ddobokki基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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